{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/comparison-of-vca-and-gaee-algorithms-for","title":"Comparison of VCA and GAEE algorithms for Endmember Extraction","arxiv_id":"1805.10644","date":"2018-05-27","proceeding":null,"authors":["Douglas Winston. R. S.","Gustavo T. Laureano","Celso G. Camilo Jr"],"abstract":"Endmember Extraction is a critical step in hyperspectral image analysis and\nclassification. It is an useful method to decompose a mixed spectrum into a\ncollection of spectra and their corresponding proportions. In this paper, we\nsolve a linear endmember extraction problem as an evolutionary optimization\ntask, maximizing the Simplex Volume in the endmember space. We propose a\nstandard genetic algorithm and a variation with In Vitro Fertilization module\n(IVFm) to find the best solutions and compare the results with the state-of-art\nVertex Component Analysis (VCA) method and the traditional algorithms Pixel\nPurity Index (PPI) and N-FINDR. The experimental results on real and synthetic\nhyperspectral data confirms the overcome in performance and accuracy of the\nproposed approaches over the mentioned algorithms.","url_abs":"http://arxiv.org/abs/1805.10644v1","url_pdf":"http://arxiv.org/pdf/1805.10644v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"comparison-of-vca-and-gaee-algorithms-for","repo_url":"https://github.com/douglaswinstonr/GAEE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"hyperspectral-image-analysis","task_name":"Hyperspectral image analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}